动态调整聚类数，减少通信开销且不降模型精度
An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning
- 根据训练需求自适应调节客户端聚类数量
- 在非独立同分布数据上通信成本降低近50%
- 适合资源受限场景下的高效联邦学习应用
联邦学习是一种新型去中心化学习架构。训练过程中，客户端与服务器需持续上传和下载模型参数，消耗大量网络传输资源。部分方法通过聚类选取更具代表性的客户端参与训练，以减少参与人数并保持精度。然而，在联邦学习中，聚类数量对训练效果的影响难以预判。为此，本文提出动态调整聚类数的方法，以获得最优分组效果。该方法可显著减少参与训练的用户数量，从而降低通信成本，同时保持模型性能。实验在非独立同分布的手写数字识别数据集上验证，相比传统联邦学习，通信开销降低近50%，模型精度未受影响。
原文摘要 · Abstract (English)
Federated learning is a novel decentralized learning architecture. During the training process, the client and server must continuously upload and receive model parameters, which consumes a lot of network transmission resources. Some methods use clustering to find more representative customers, select only a part of them for training, and at the same time ensure the accuracy of training. However, in federated learning, it is not trivial to know what the number of clusters can bring the best training result. Therefore, we propose to dynamically adjust the number of clusters to find the most ideal grouping results. It may reduce the number of users participating in the training to achieve the effect of reducing communication costs without affecting the model performance. We verify its experimental results on the non-IID handwritten digit recognition dataset and reduce the cost of communication and transmission by almost 50% compared with traditional federated learning without affecting the accuracy of the model.
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